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Perceptions Toward Using Artificial Intelligence and Technology for Asthma Attack Risk Prediction: Qualitative Exploration of Maori Views

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JMIR Formative Research 2024年 8卷 e59811页

作者： Jayamini, Widana Kankanamge Darsha Mirza, Farhaan Bidois-Putt, Marie-Claire Naeem, M. Asif Chan, Amy Hai Yan Department of Computer Science School of Engineering Computer and Mathematical Sciences Auckland University of Technology Auckland New Zealand Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Kelaniya Sri Lanka School of Pharmacy Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Department of Data Science & Artificial Intelligence National University of Computer and Emerging Sciences (NUCES) Islamabad Pakistan

Background: Asthma is a significant global health issue, impacting over 500,000 individuals in New Zealand and disproportionately affecting Maori communities in New Zealand, who experience worse asthma symptoms and attacks. Digital technologies, including artificial intelligence (AI) and machine learning (ML) models, are increasingly popular for asthma risk prediction. However, these AI models may underrepresent minority ethnic groups and introduce bias, potentially exacerbating disparities. Objective: This study aimed to explore the views and perceptions that Maori have toward using AI and ML technologies for asthma self-management, identify key considerations for developing asthma attack risk prediction models, and ensure Maori are represented in ML models without worsening existing health inequities. Methods: Semistructured interviews were conducted with 20 Maori participants with asthma, 3 male and 17 female, aged 18-76 years. All the interviews were conducted one-on-one, except for 1 interview, which was conducted with 2 participants. Altogether, 10 web-based interviews were conducted, while the rest were kanohi ki te kanohi (face-to-face). A thematic analysis was conducted to identify the themes. Further, sentiment analysis was carried out to identify the sentiments using a pretrained Bidirectional Encoder Representations from Transformers model. Results: We identified four key themes: (1) concerns about AI use, (2) interest in using technology to support asthma, (3) desired characteristics of AI-based systems, and (4) experience with asthma management and opportunities for technology to improve care. AI was relatively unfamiliar to many participants, and some of them expressed concerns about whether AI technology could be trusted, kanohi ki te kanohi interaction, and inadequate knowledge of AI and technology. These concerns are exacerbated by the Maori experience of colonization. Most of the participants were interested in using technology to support their as

关键词： artificial intelligence asthma risk prediction health system development machine learning maori perceptions mobile phone

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Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education

Deep Learning-Based E-Learning Solution for Identifying and ...

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International Research Conference on Smart Computing and Systems engineering (SCSE)

作者： D.P.H Arunoda S.R Walpola S.M.I Piumira A.D.M.P Athukorala Thusithanjana Thilakarathna Sanjeevi Chandrasiri Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students’ interactions (attention, emotions) with the system to identify students’ ability to use the learning tool, identifying gaps in students’ knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students’ knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.

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An Anomaly Detection System Based on Clustering and Fuzzy Set Theory in VANETs 8th

An Anomaly Detection System Based on Clustering and Fuzzy Se...

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： Kuchaki Rafsanjani, Marjan Fatemidokht, Hamideh Balas, Valentina Emilia Batth, Ranbir Singh Department of Computer Science Faculty of Mathematics and Computer Shahid Bahonar University of Kerman Kerman Iran Department of Applied Mathematics Young Research Society Shahid Bahonar University of Kerman Kerman Iran Department of Automatics and Applied Software Faculty of Engineering Aurel Vlaicu University of Arad Arad Romania School of Computer Science and Engineering Lovely Professional University Jalandhar India

ISBN: (纸本)9783030521899

In vehicular ad hoc network (VANET), which is considered as a type of mobile ad hoc network (MANET), communication occurs between neighbor vehicles and also roadside equipment and vehicles. Due to the characteristics of VANET, there are chances of a number possible attack in this network. In this paper, an anomaly detection system that uses clustering and fuzzy set theory to defend the Denial of Service (DoS) attack is presented. Results of simulation represent that the proposed algorithm arrives to the high detection rate. © 2021, Springer Nature Switzerland AG.

关键词： Fuzzy set theory

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Enhancing Forensic Audio Transcription with Neural Network-Based Speaker Diarization and Gender Classification 10

Enhancing Forensic Audio Transcription with Neural Network-B...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Ullah, Rahmat Asghar, Ikram Malik, Hassan Evans, Gareth Ahmad, Jawad Roberts, Dorothy Anne School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom School of Computing Engineering and Digital Technologies Teesside University MiddlesbroughTS1 3BX United Kingdom School of Computing Sciences University of East Anglia NorwichNR4 7TJ United Kingdom Uiversity of South Wales Faculty of Computing Engineering and Science PontypriddCF37 1DL United Kingdom Prince Mohammad Bin Fahd University Cybersecurity Center Al Khobar Saudi Arabia Posib Ltd y Gilfach Flintshire NercwysCH7 4EL United Kingdom

ISBN: (纸本)9798331532895

Forensic audio transcription is often compromised by low-quality recordings, where indistinct speech can hinder the accuracy of conventional Automatic Speech Recognition (ASR) systems. This study addresses this limitation by developing a machine learning-based approach to improve speaker diarization, a process critical for distinguishing between speakers in sensitive audio data. Previous research highlights the inadequacy of traditional ASR in forensic settings, particularly where audio quality is poor and speaker overlap is common. This paper presents a neural network specifically designed for gender classification, using 20 key acoustic features extracted from real forensic audio data. The model architecture includes input, hidden, and output layers tailored to differentiate male and female voices, with dropout regularization to prevent overfitting and hyperparameter optimization ensuring robust generalization across test data. The neural network achieved an average recall of 86.81%, F1 score of 85.67%, precision of 87.95%, and accuracy of 86.83% across varied audio conditions. This model significantly improves transcription accuracy, reducing errors in legal contexts and supporting judicial processes with more reliable, interpretable evidence from sensitive audio data. © 2024 IEEE.

关键词： Speech recognition

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Digital Image Recovery Based on Lifting Wavelet Transform 1

Digital Image Recovery Based on Lifting Wavelet Transform

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1st International Conference on engineering and Technology, ICoEngTech 2021

作者： Taha, Taha Basheer Taha, Dujan B. Ngadiran, Ruzelita Ehkhan, Phaklen IT Department Faculty of Sciences TISHK International University Erbil Iraq Software Engineering Department College of Computer Science and Mathematics Mosul University Iraq Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack. © Published under licence by IOP Publishing Ltd.

关键词： Digital storage

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Unveiling Deception: Different Strategies for Combatting Arabic Fake News

Unveiling Deception: Different Strategies for Combatting Ara...

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Information Technology (ACIT)

作者： Nada Khattab Ahmad Al-Qerem Shadi Nashwan Issam Jebreen Mohammed Rajab Mohammad Alauthman Mohammad Jeadi Ali Elrashidi Computer Science Department Faculty of Information Technology Zarqa University Zarqa Jordan Cybersecurity Department Faculty of Information Technology Middle East University Amman Jordan University Headquarter University of Anbar Ramadi Iraq Department of Information Security University of Petra Amman Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

the proliferation of fake news has become a widespread problem in the contemporary era of information, posing possible harm to public discussions, political stability, and community confidence. To tackle this difficulty, it is necessary to develop strong and effective detection methods that can accurately recognize misinformation in several languages, including Arabic. The aim of this study is to create and assess different models specifically designed for the identification of fake news in Arabic *** of deep Learning models like Recurrent Neural Networks (RNN), Long short-term memory (LSTM), Bidirectional long short term memory (Bi-LSTM) and convolutional neural network (CNN) are used in this study. On the other hand, some of the traditional machine learning models like k-nearest neighbors (KNN), Random Forests (RF), Extreme Gradient Boosting (XGBoost), decision tree (DT) and multi-layer perceptron (MLP) are also used. Also, transformers, like Generative pre-trained Transformer 3 (Gpt-3) and Bidirectional Encoder Representations from Transformers (BERT), are used as a feature extraction to convert text into numerical representation and assign different number to each token. All of these models were used in evaluation which of them is the best model in the process of fake news detection for the Arabic Language *** findings demonstrate strong performance across deep learning models, with CNN-LSTM, Bi-LSTM and CNN-Bi-LSTM exhibiting the best accuracy rates of 93.2% ,92.6%, 93.6% respectively. The LSTM model achieves a moderate accuracy of 87.4%, whilst the RNN model demonstrates a more modest accuracy of 56.8%. The results emphasize the capability of deep learning methods in reducing the dissemination of fake information in Arabic-language datasets.

关键词： Deep learning Radio frequency Accuracy Nearest neighbor methods Transformers Stability analysis Numerical models Convolutional neural networks Fake news Random forests

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Random forest‑based prediction of stroke outcome

arXiv

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arXiv 2024年

作者： Fernandez‑Lozano, Carlos Hervella, Pablo Mato‑Abad, Virginia Rodríguez‑Yáñez, Manuel Suárez‑Garaboa, Sonia López‑Dequidt, Iria Estany‑Gestal, Ana Sobrino, Tomás Campos, Francisco Castillo, José Rodríguez‑Yáñez, Santiago Iglesias‑Rey, Ramón Department of Computer Science and Information Technologies Faculty of Computer Science CITIC-Research Center of Information and Communication Technologies Universidade da Coruña A Coruña Spain SERGAS Universidade da Coruña A Coruña Spain Santiago de Compostela Spain Software Engineering Laboratory Department of Computer Science and Information Technologies Faculty of Computer Science University of A Coruña Campus de Elviña A Coruña15071 Spain Hospital Clínico Universitario Rúa Travesa da Choupana s/n Santiago de Compostela15706 Spain Santiago de Compostela Spain

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3‑months after admission. The dataset consisted of patients with ischemic stroke (IS) and non‑traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/ morbidity according to the following groups: (1) IS + ICH, (2) IS, and (3) ICH. A total of 6022 patients were included: 4922 (mean age 71.9 ± 13.8 years) with IS and 1100 (mean age 73.3 ± 13.1 years) with ICH. NIHSS at 24, 48 h and axillary temperature at admission were the most important variables to consider for evolution of patients at 3‑months. IS + ICH group was the most stable for mortality prediction [0.904 ± 0.025 of area under the receiver operating characteristics curve (AUC)]. IS group presented similar results, although variability between experiments was slightly higher (0.909 ± 0.032 of AUC). ICH group was the one in which RF had more problems to make adequate predictions (0.9837 vs. 0.7104 of AUC). There were no major differences between IS and IS + ICH groups according to morbidity prediction (0.738 and 0.755 of AUC) but, after checking normality with a Shapiro Wilk test with the null hypothesis that the data follow a normal distribution, it was rejected with W = 0.93546 (p‑value © 2024, CC BY.

关键词： Forecasting

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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International Conference on engineering and Emerging Technologies (ICEET)

作者： Aqib Hassan Vengas Memon Farhan Bashir Shaikh Saad Alahmari Mansoor Iqbal Farooq Azam School of computing and mathematical sciences University of Greenwich London United Kingdom Faculty of Information and Communication Tech. Universiti Tunku Abdul Rahman Perak Malaysia Department of Computer Science Northern Border University Arar Saudi Arabia Department of Computer Science University of Wah Wah Cantt Pakistan Dept. of Computer Software Engineering NUST Islamabad Pakistan

ISBN: (数字)9798331532895

ISBN: (纸本)9798331532901

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability.

关键词： TCP Transport protocols Protocols Force Packet loss Throughput Real-time systems Stability analysis Reliability Low latency communication

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GustosonicSense: Towards understanding the design of playful gustosonic eating experiences

arXiv

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arXiv 2024年

作者： Wang, Yan Obie, Humphrey O. Li, Zhuying Salim, Flora D. Grundy, John Mueller, Florian Floyd Exertion Game Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia Department of Computing Technologies Swinburne University of Technology HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia School of Computer Science and Engineering Southeast University Jiangsu Nanjing China Sydney SydneyNSW Australia HumaniSE Lab Department of Software Systems and Cybersecurity Monash University MelbourneVIC Australia Exertion Games Lab Department of Human-Centred Computing Monash University MelbourneVIC Australia

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food. © 2024, CC BY.

关键词： Learning algorithms

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Multimodal Fusion for Enhanced Parkinson's Disease Screening: Integrating Brain MRI, Hand Drawing, Facial Expressions, and Voice Analysis

Multimodal Fusion for Enhanced Parkinson's Disease Screening...

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Advancements in Computing (ICAC), International Conference on

作者： Naveen Warnakulasuriya Shalinika De Silva Janani Madushika Sadali Gamage Sanath Jayawardena Anuradha Karunasena Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Computer Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

Parkinson's disease (PD) profoundly impacts millions in Sri Lanka, emphasizing the importance of early detection for better patient outcomes. We introduce “NeuraTrace PD,” an innovative application for early PD screening, combining brain MRI, hand-drawn image analysis, facial expression assessment, and voice analysis. For brain MRI analysis, we employed a Graph Convolutional Network (GCN), achieving an impressive accuracy of 91% and identifying distinct graph connectivity patterns using graph theory. We also use a deep Convolutional Neural Network (CNN) to reach 90% accuracy in analyzing hand-drawn images, while optimizing image resolution for efficiency and information retention. Facial expression analysis is a vital component, leveraging CNN and Constrained Local Models (CLM) to accurately extract Facial Action Units (FAUs), which contribute to a remarkable 95% accuracy using the XGBoost machine learning model. We introduce a Machine Learning (ML) algorithm and advanced signal processing to enhance voice feature analysis, offering a thorough understanding and classification of PD patients. It reaches 99% accuracy. In summary, our approach holds significant promise for advancing early PD diagnosis, ultimately improving the quality of life for those affected by this challenging condition.

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